True positives(TPs) are those entities
which are correctly classified by a model as positive instances of the
concept being modelled (e.g., the model identifies them as a case of
fraud, and they indeed are a case of fraud). False positives(FPs) are classified as positive instances by
the model, but in fact are known not to be. Similarly, true
negatives(TNs) are those entities correctly
classified by the model as not being instances of the concept, and
false negatives(FNs) are classified as
not being instances, but are in fact know to be. These are the basic
measures of the performance of a model. These basic measures are often
presented in the form of a confusion matrix, produced
using a contingency table.